التفاصيل البيبلوغرافية
| العنوان: |
ML-Based Framework to Predict the Severity of the Symptomatology in Patients with Post-Acute COVID-19 Syndrome. |
| المؤلفون: |
NITULESCU, Adina, CRISAN-VIDA, Mihaela, TUDORAN, Cristina, STOICU-TIVADAR, Lacramioara |
| المصدر: |
Studies in Health Technology & Informatics; 2024, Vol. 321, p99-103, 5p |
| مستخلص: |
The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of symptoms following the acute COVID-19 infection. The patient's baseline clinical features were used as predictors. The analysis showed that older patients with comorbidities are at higher risk of developing more long-lasting post COVID-19 symptoms. Further integration with a personal monitoring device and combination with the Fast Healthcare Interoperability Resources extends the standardization, interoperability and possibility of integration and harmonization with other hospital systems. By employing advanced machine learning techniques, insights can be derived and further examined to improve the outcome and early treatment options for patients. [ABSTRACT FROM AUTHOR] |
|
Copyright of Studies in Health Technology & Informatics is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |